def __init__(self, gt, predict, threshold = 0.5):
self.gt = gt
self.adjusted_predict = adjust_predict(predict)
self.relevant_data = exclude_ignore_label(gt, self.adjusted_predict)
self.nlables = get_number_of_labels(gt)
self.apr = apr(self.relevant_data[0], self.relevant_data[1])
self.auc_score = roc_auc(self.relevant_data[0], self.relevant_data[1])
self.quality = np.array((self.apr[0], self.apr[1], self.apr[2], self.auc_score))
self.roc_curve = draw_roc_curve(self.relevant_data[0], self.relevant_data[1])
#
#
#
#c = config(p[0], p[1])
#c.show_quality()
#
#d = config(q[0], q[1])
#d.show_quality()
I = read_h5("/home/stamylew/volumes/trimaps/50cube2_tri.h5", "50cube2_tri")
II = read_h5("/home/stamylew/src/autocontext/prediction/cache/smallcubes_probs.h5", "exported_data")
II = adjust_predict(II)
q = exclude_ignore_label(I,II)
c = predict_class(q[0], q[1])
c.show_quality()
print c.return_quality()
#III = read_h5("/home/stamylew/volumes/training_data/50cube3_bp.h5", "all_labels/n3/w_3_2_1")
#III = adjust_predict_file(III)
#plt.figure()
#plt.imshow(I[11])
#
#plt.figure()
#plt.imshow(II[11])
#
#plt.show()